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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 647–663Cite as

Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

Abstract

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods.

S. Zhao—This work was done when Shizhen Zhao was an intern at Tencent Youtu Lab.

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Acknowledgment

This work was supported by the National Key R&D Program of China No. 2018YFB1004602 and the Project of the National Natural Science Foundation of China No. 61876210.

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Authors and Affiliations

  1. Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Shizhen Zhao, Changxin Gao, Chuchu Han & Nong Sang

  2. Tencent Youtu Lab, Shanghai, China

    Jun Zhang, Hao Cheng, Xinyang Jiang, Xiaowei Guo & Xing Sun

  3. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

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  1. Shizhen Zhao
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  2. Changxin Gao
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  3. Jun Zhang
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Correspondence to Changxin Gao .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Zhao, S. et al. (2020). Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_39

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_39

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